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Using Machine Learning for Early Recognition and Personalized Treatment of Acute Kidney Injury

使用机器学习对急性肾损伤进行早期识别和个性化治疗

基本信息

批准号：
10683199
负责人：
Matthew Michael Churpek
金额：
$ 69.79万
依托单位：
UNIVERSITY OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2026-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10683199
关键词：
Acute Renal Failure with Renal Papillary Necrosis Admission activity Albuminuria Algorithms Biological Markers Blood Urea Nitrogen Caring Clinical Code Consensus Consult Creatinine Data Detection Development Discrimination Early Diagnosis Early Intervention Electronic Health Record Electronics Future Gold Growth Guidelines Health system Healthcare Systems Hospitalization Hospitals Hour Hypotension Incidence Injury Injury to Kidney Inpatients Intervention Kidney Knowledge LCN2 gene Length of Stay Machine Learning Manuals Measures Methods Modeling Morbidity - disease rate Multi-Institutional Clinical Trial Natural Language Processing Nephrology Neural Network Simulation Outcome Output Pathway interactions Patient Care Patient-Focused Outcomes Patients Pharmaceutical Preparations Phenotype Predictive Value Prevalence Recommendation Research Risk Risk Assessment Risk Factors Running Serum Syndrome TIMP2 gene Text Time Tubular formation United States Urine Work clinical practice cohort comorbidity cost cost estimate deep learning deep learning algorithm deep learning model deep neural network detection method diagnosis standard electronic structure forest high risk hospital readmission improved improved outcome machine learning algorithm machine learning method mortality nephrotoxicity novel novel marker participant enrollment personalized care personalized medicine predictive marker readmission risk risk prediction model risk stratification success tool urinary

项目摘要

PROJECT SUMMARY Acute kidney injury (AKI) occurs in up to 20% of hospitalized patients and is associated with increased risk of readmission, morbidity, and mortality. The estimated annual cost of AKI care in the US is over 10 billion dollars, and, with the incidence rising, these costs will continue to increase. The current gold standards for diagnosing AKI, creatinine and urine output, are often delayed in their recognition of tubular injury. Prior work on AKI has typically focused on patients who have already developed AKI based on these standards, and interventions at this late time point have had mixed success. In contrast, emerging data suggest that intervening earlier can improve outcomes. Therefore, it is critical to optimize the early detection of AKI in hospitalized patients. We have previously developed a machine learning tool to identify patients at high risk of severe (stage 2 or greater) AKI more than a day earlier than clinically apparent using structured electronic health record (EHR) data. Although more accurate than prior methods, it suffers from a high rate of false positives, which limits its value in clinical practice. There is a large amount of valuable information that is stored in unstructured free-text fields (e.g., clinical notes) that could be utilized using natural language processing (NLP) within advanced deep learning neural network models that could significantly improve the detection of early AKI. Furthermore, there are established and emerging kidney injury biomarkers that could be combined with EHR-based models to improve accuracy even further. Finally, it remains unclear what interventions will have the best chance of decreasing the risk for developing severe AKI in high-risk patients. A better understanding of which interventions are of greatest benefit to specific patients is critical for improving the outcomes of patients at risk of AKI. The objective of this project is to develop novel tools to improve the identification and treatment of patients at high risk of AKI using a large, multicenter cohort. In Aim 1, we will use NLP and deep learning algorithms to develop a model to predict severe AKI across four health systems. In Aim 2, we will silently run the best- performing model developed in Aim 1 in real-time to identify high-risk patients. Manual retrospective chart review will be performed on a cohort of the highest risk patients to determine both the proportion of patients who receive guideline-based care as well as the association between receipt of guideline-based care and outcomes. We will also identify novel phenotypes of patients who are particularly helped or harmed by specific guideline-based interventions. Finally, in Aim 3, we will collect kidney injury biomarkers in the highest-risk patients to determine the added value of biomarkers to EHR-based models alone. Our proposal will provide clinicians with new tools to identify patients at risk of AKI earlier and more accurately. It will also provide evidence for which interventions are most likely to improve patient outcomes. This will result in earlier, more personalized care for patients at high risk of AKI, which will lead to decreased costs, morbidity, and mortality.

项目摘要急性肾损伤（阿基）发生在高达20%的住院患者中，并且与以下风险增加相关：再入院率、发病率和死亡率。据估计，美国每年用于阿基护理的费用超过100亿美元，而随着发病率的上升，这些成本将继续增加。目前诊断的黄金标准阿基、肌酸酐和尿量通常延迟对肾小管损伤的识别。之前对阿基的研究通常侧重于根据这些标准已经发生阿基的患者，这个较晚的时间点取得了好坏参半的成功。相比之下，新出现的数据表明，改善成果。因此，优化住院患者阿基的早期检测至关重要。我们之前已经开发了一种机器学习工具来识别严重（2期）高风险的患者。或更大）阿基比使用结构化电子健康记录（EHR）的临床表现早一天以上数据虽然比现有方法更准确，但它具有高的误报率，这限制了其应用。临床实践价值。有大量有价值的信息存储在非结构化的自由文本中字段（例如，临床笔记），可以使用自然语言处理（NLP）在高级深度学习可以显著改善早期阿基检测的神经网络模型。而且是已经建立和正在出现的肾损伤生物标志物，可以与基于EHR的模型相结合，进一步提高了准确性。最后，目前还不清楚什么干预措施最有可能降低高危患者发生严重阿基的风险。更好地了解哪些干预措施对特定患者的最大获益对于改善阿基风险患者的结局至关重要。该项目的目标是开发新的工具，以改善患者的识别和治疗使用大型多中心队列研究AKI高风险患者。在目标1中，我们将使用NLP和深度学习算法，开发一个模型来预测四个卫生系统中的严重阿基。在目标2中，我们将默默地运行最好的- 实时执行目标1中开发的模型，以识别高风险患者。手动回顾性病历审查将在最高风险患者队列中进行，以确定接受基于指南的护理以及基于指南的护理的接受与结果之间的关联。我们将还确定了特别有助于或损害基于特定指南的患者的新表型，干预措施。最后，在目标3中，我们将收集最高风险患者的肾损伤生物标志物以确定生物标志物对EHR模型的附加值。我们的建议将为临床医生提供新的工具更早、更准确地识别有阿基风险的患者。它还将提供证据，最有可能改善患者的预后。这将为高血压患者提供更早、更个性化的护理。阿基风险，这将导致成本、发病率和死亡率降低。